{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-12-08T01:22:23.960067300Z",
     "start_time": "2023-12-08T01:21:48.894365400Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torchvision import datasets,transforms\n",
    "import torch.nn as nn\n",
    "import torchvision\n",
    "import os\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "data_transform = {\n",
    "    'train':transforms.Compose([\n",
    "             transforms.Resize(230),\n",
    "             transforms.CenterCrop(224),\n",
    "             transforms.RandomHorizontalFlip(),\n",
    "             transforms.ToTensor(),\n",
    "             transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))\n",
    "           ]),\n",
    "    'test':transforms.Compose([\n",
    "        transforms.Resize(230),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))\n",
    "    ])\n",
    "}"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T01:41:43.444662400Z",
     "start_time": "2023-12-08T01:41:43.241379200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "(275, 70)"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_path = 'F:/图像识别数据集/archive'\n",
    "data_path_train = os.path.join(data_path,'train')\n",
    "data_path_val = os.path.join(data_path,'val')\n",
    "trainset = datasets.ImageFolder(data_path_train,transform=data_transform['train'])\n",
    "testset = datasets.ImageFolder(data_path_val,transform=data_transform['test'])\n",
    "len(trainset),len(testset)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T01:46:41.057807300Z",
     "start_time": "2023-12-08T01:46:40.519803100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "(55, 14)"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_loader = torch.utils.data.DataLoader(trainset,batch_size=5,num_workers=4,shuffle=True)\n",
    "test_loader = torch.utils.data.DataLoader(testset,batch_size=5,num_workers=4,shuffle=True)\n",
    "len(train_loader),len(test_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T01:49:34.681586900Z",
     "start_time": "2023-12-08T01:49:34.657574600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "def imshow(inputs):\n",
    "    inputs = inputs/2+0.5\n",
    "    inputs = inputs.numpy().transpose(1,2,0)\n",
    "    plt.imshow(inputs)\n",
    "    plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T01:50:39.680247600Z",
     "start_time": "2023-12-08T01:50:39.641250700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "inputs,labels = next(iter(test_loader))\n",
    "imshow(torchvision.utils.make_grid(inputs))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T01:54:50.274055500Z",
     "start_time": "2023-12-08T01:54:25.214915300Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 导入模型"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## alexnet网络"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AlexNet(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "    (4): ReLU(inplace=True)\n",
      "    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (7): ReLU(inplace=True)\n",
      "    (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (9): ReLU(inplace=True)\n",
      "    (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (11): ReLU(inplace=True)\n",
      "    (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))\n",
      "  (classifier): Sequential(\n",
      "    (0): Dropout(p=0.5, inplace=False)\n",
      "    (1): Linear(in_features=9216, out_features=4096, bias=True)\n",
      "    (2): ReLU(inplace=True)\n",
      "    (3): Dropout(p=0.5, inplace=False)\n",
      "    (4): Linear(in_features=4096, out_features=4096, bias=True)\n",
      "    (5): ReLU(inplace=True)\n",
      "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "alexnet = torchvision.models.alexnet(pretrained=True)\n",
    "print(alexnet)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T02:38:49.461356400Z",
     "start_time": "2023-12-08T02:38:48.267558600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [],
   "source": [
    "for p in alexnet.parameters():\n",
    "    p.requires_grad = False\n",
    "alexnet.classifier = nn.Sequential(\n",
    "    nn.Dropout(p=0.5,inplace=False),\n",
    "    nn.Linear(9216,4096,bias=True),\n",
    "    nn.ReLU(inplace=False),\n",
    "    nn.Dropout(p=0.5,inplace=False),\n",
    "    nn.Linear(4096,4096,bias=True),\n",
    "    nn.ReLU(inplace=False),\n",
    "    nn.Linear(4096,2,bias=True)\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T02:38:52.157230600Z",
     "start_time": "2023-12-08T02:38:51.774244800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [],
   "source": [
    "import datetime\n",
    "def train_loop(model,loss_fn,optimizer,n_epochs,train_loader):\n",
    "    for epoch in range(1,n_epochs+1):\n",
    "        loss_train = 0.0\n",
    "        for i,data in enumerate(train_loader,1):\n",
    "            imgs,labels = data\n",
    "            imgs,labels = imgs.cuda(),labels.cuda()\n",
    "            outputs = model(imgs)\n",
    "            loss = loss_fn(outputs,labels)\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            loss_train+=loss.item()\n",
    "            if i%5 ==0 or i==1:\n",
    "                print('{}, epoch:{}, i:{}, 训练损失:{}'.format(datetime.datetime.now(),epoch,i,loss_train/len(train_loader)))\n",
    "                loss_train = 0.0"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T02:38:53.090562400Z",
     "start_time": "2023-12-08T02:38:53.071567200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-12-08 10:38:58.902461, epoch:1, i:1, 训练损失:0.011156657609072598\n",
      "2023-12-08 10:38:59.055432, epoch:1, i:5, 训练损失:0.049368813904848964\n",
      "2023-12-08 10:38:59.242431, epoch:1, i:10, 训练损失:0.06617088642987339\n",
      "2023-12-08 10:38:59.423479, epoch:1, i:15, 训练损失:0.09001153599132192\n",
      "2023-12-08 10:38:59.603430, epoch:1, i:20, 训练损失:0.033067137587138196\n",
      "2023-12-08 10:38:59.782477, epoch:1, i:25, 训练损失:0.12653038806535982\n",
      "2023-12-08 10:38:59.964460, epoch:1, i:30, 训练损失:0.05280409861694683\n",
      "2023-12-08 10:39:00.145432, epoch:1, i:35, 训练损失:0.046606387875296855\n",
      "2023-12-08 10:39:00.328482, epoch:1, i:40, 训练损失:0.040863893655213444\n",
      "2023-12-08 10:39:00.507434, epoch:1, i:45, 训练损失:0.022599723664197054\n",
      "2023-12-08 10:39:00.688435, epoch:1, i:50, 训练损失:0.017236875539476223\n",
      "2023-12-08 10:39:00.868473, epoch:1, i:55, 训练损失:0.047845329479737714\n",
      "2023-12-08 10:39:06.431477, epoch:2, i:1, 训练损失:0.010652547532861883\n",
      "2023-12-08 10:39:06.585484, epoch:2, i:5, 训练损失:0.009936325793916529\n",
      "2023-12-08 10:39:06.774462, epoch:2, i:10, 训练损失:0.01965547962622209\n",
      "2023-12-08 10:39:06.956478, epoch:2, i:15, 训练损失:0.020475527915087614\n",
      "2023-12-08 10:39:07.137477, epoch:2, i:20, 训练损失:0.01648001860488545\n",
      "2023-12-08 10:39:07.316479, epoch:2, i:25, 训练损失:0.04119447215714238\n",
      "2023-12-08 10:39:07.497430, epoch:2, i:30, 训练损失:0.038041133772243156\n",
      "2023-12-08 10:39:07.676467, epoch:2, i:35, 训练损失:0.016387688808820463\n",
      "2023-12-08 10:39:07.859476, epoch:2, i:40, 训练损失:0.01878808228807016\n",
      "2023-12-08 10:39:08.037430, epoch:2, i:45, 训练损失:0.024204432672228327\n",
      "2023-12-08 10:39:08.218477, epoch:2, i:50, 训练损失:0.012291888147592545\n",
      "2023-12-08 10:39:08.396465, epoch:2, i:55, 训练损失:0.02451939462599429\n"
     ]
    }
   ],
   "source": [
    "import torch.optim as optim\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer=optim.SGD(alexnet.parameters(),lr=0.001,momentum=0.9)\n",
    "train_loop(alexnet.cuda(),optimizer=optimizer,loss_fn=loss_fn,n_epochs=2,train_loader=train_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T02:39:09.051435200Z",
     "start_time": "2023-12-08T02:38:54.279442100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [],
   "source": [
    "def test_loop(model,test_loader):\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    for imgs,labels in test_loader:\n",
    "        imgs,labels = imgs.cuda(),labels.cuda()\n",
    "        outputs = model(imgs)\n",
    "        _,preds = torch.max(outputs,dim=1)\n",
    "        correct += int((preds==labels).sum())\n",
    "        total += labels.shape[0]\n",
    "    print('精度:{:.3f}%'.format((correct/total)*100))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T02:39:13.230018400Z",
     "start_time": "2023-12-08T02:39:13.217874300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "精度:95.714%\n"
     ]
    }
   ],
   "source": [
    "test_loop(alexnet.cuda().eval(),test_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T02:39:20.807362100Z",
     "start_time": "2023-12-08T02:39:15.281364100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "真实值---预测值\n",
      "小猫 --- 小狗\n",
      "小狗 --- 小狗\n",
      "小猫 --- 小猫\n",
      "小狗 --- 小狗\n",
      "小狗 --- 小狗\n"
     ]
    }
   ],
   "source": [
    "outputs = alexnet.cuda().eval()(inputs.cuda())\n",
    "_,preds = torch.max(outputs,dim=1)\n",
    "imshow(torchvision.utils.make_grid(inputs))\n",
    "print('真实值---预测值')\n",
    "class_names = ['小猫','小狗']\n",
    "for i,j in zip(labels,preds):\n",
    "    print(class_names[i],'---',class_names[j])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T02:39:23.020384600Z",
     "start_time": "2023-12-08T02:39:22.694365600Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## vgg网络模型"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VGG(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (3): ReLU(inplace=True)\n",
      "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (6): ReLU(inplace=True)\n",
      "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (8): ReLU(inplace=True)\n",
      "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (11): ReLU(inplace=True)\n",
      "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (13): ReLU(inplace=True)\n",
      "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (15): ReLU(inplace=True)\n",
      "    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (18): ReLU(inplace=True)\n",
      "    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (20): ReLU(inplace=True)\n",
      "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (22): ReLU(inplace=True)\n",
      "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (25): ReLU(inplace=True)\n",
      "    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (27): ReLU(inplace=True)\n",
      "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (29): ReLU(inplace=True)\n",
      "    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
      "  (classifier): Sequential(\n",
      "    (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): Dropout(p=0.5, inplace=False)\n",
      "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
      "    (4): ReLU(inplace=True)\n",
      "    (5): Dropout(p=0.5, inplace=False)\n",
      "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "96vgg = torchvision.models.vgg16(pretrained=True)\n",
    "print(vgg)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T02:41:14.616730300Z",
     "start_time": "2023-12-08T02:41:12.012960200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [],
   "source": [
    "for p in vgg.parameters():\n",
    "    p.requires_grad = False\n",
    "\n",
    "vgg.classifier = nn.Sequential(\n",
    "    nn.Linear(25088,4096,bias=True),\n",
    "    nn.ReLU(inplace=True),\n",
    "    nn.Dropout(p=0.5,inplace=False),\n",
    "    nn.Linear(4096,4096,bias=True),\n",
    "    nn.ReLU(inplace=True),\n",
    "    nn.Dropout(p=0.5,inplace=False),\n",
    "    nn.Linear(4096,2,bias=True)\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T02:44:19.122230200Z",
     "start_time": "2023-12-08T02:44:18.077239300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-12-08 10:45:50.164654, epoch:1, i:1, 训练损失:0.011017810214649548\n",
      "2023-12-08 10:45:50.845700, epoch:1, i:5, 训练损失:0.048861899159171364\n",
      "2023-12-08 10:45:51.682652, epoch:1, i:10, 训练损失:0.05492821498350663\n",
      "2023-12-08 10:45:52.515681, epoch:1, i:15, 训练损失:0.039236123995347455\n",
      "2023-12-08 10:45:53.351657, epoch:1, i:20, 训练损失:0.03715639249844985\n",
      "2023-12-08 10:45:54.180654, epoch:1, i:25, 训练损失:0.016224383088675413\n",
      "2023-12-08 10:45:55.007653, epoch:1, i:30, 训练损失:0.018307244777679442\n",
      "2023-12-08 10:45:55.827702, epoch:1, i:35, 训练损失:0.005222617089748383\n",
      "2023-12-08 10:45:56.648650, epoch:1, i:40, 训练损失:0.010541616447947242\n",
      "2023-12-08 10:45:57.470681, epoch:1, i:45, 训练损失:0.0033708511767062276\n",
      "2023-12-08 10:45:58.292695, epoch:1, i:50, 训练损失:0.011098938655446876\n",
      "2023-12-08 10:45:59.119686, epoch:1, i:55, 训练损失:0.0034455861896276474\n",
      "2023-12-08 10:46:04.556012, epoch:2, i:1, 训练损失:8.019452745264227e-05\n",
      "2023-12-08 10:46:05.228035, epoch:2, i:5, 训练损失:0.0027524355798959734\n",
      "2023-12-08 10:46:06.051015, epoch:2, i:10, 训练损失:0.0009492464681071313\n",
      "2023-12-08 10:46:06.873011, epoch:2, i:15, 训练损失:0.004808973080732606\n",
      "2023-12-08 10:46:07.695057, epoch:2, i:20, 训练损失:0.002190967648163099\n",
      "2023-12-08 10:46:08.517014, epoch:2, i:25, 训练损失:0.012395276029763574\n",
      "2023-12-08 10:46:09.342059, epoch:2, i:30, 训练损失:0.00392022769136185\n",
      "2023-12-08 10:46:10.170065, epoch:2, i:35, 训练损失:0.002169737507673827\n",
      "2023-12-08 10:46:11.003037, epoch:2, i:40, 训练损失:0.0010070367546921428\n",
      "2023-12-08 10:46:11.840039, epoch:2, i:45, 训练损失:0.007779908370734616\n",
      "2023-12-08 10:46:12.674009, epoch:2, i:50, 训练损失:0.0015625345838171515\n",
      "2023-12-08 10:46:13.511031, epoch:2, i:55, 训练损失:0.0017329328947446564\n"
     ]
    }
   ],
   "source": [
    "optimizer = optim.SGD(vgg.parameters(),lr=0.001,momentum=0.9)\n",
    "train_loop(model=vgg.cuda(),loss_fn=loss_fn,optimizer=optimizer,n_epochs=2,train_loader=train_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T02:46:14.115014700Z",
     "start_time": "2023-12-08T02:45:44.466864100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "精度:98.571%\n"
     ]
    }
   ],
   "source": [
    "test_loop(vgg.cuda().eval(),test_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T02:48:18.745496600Z",
     "start_time": "2023-12-08T02:48:11.653495800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "真实值---预测值\n",
      "小猫 --- 小猫\n",
      "小狗 --- 小狗\n",
      "小猫 --- 小猫\n",
      "小狗 --- 小狗\n",
      "小狗 --- 小狗\n"
     ]
    }
   ],
   "source": [
    "outputs = vgg.cuda().eval()(inputs.cuda())\n",
    "_,preds = torch.max(outputs,dim=1)\n",
    "imshow(torchvision.utils.make_grid(inputs))\n",
    "print('真实值---预测值')\n",
    "class_names = ['小猫','小狗']\n",
    "for i,j in zip(labels,preds):\n",
    "    print(class_names[i],'---',class_names[j])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T02:50:14.528830700Z",
     "start_time": "2023-12-08T02:50:14.178835600Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 残差网络"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet101_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet101_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ResNet(\n",
      "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
      "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (relu): ReLU(inplace=True)\n",
      "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
      "  (layer1): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (layer2): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (3): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (layer3): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (3): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (4): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (5): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (6): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (7): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (8): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (9): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (10): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (11): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (12): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (13): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (14): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (15): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (16): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (17): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (18): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (19): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (20): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (21): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (22): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (layer4): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
      "  (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "resnet = torchvision.models.resnet101(pretrained=True)\n",
    "print(resnet)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T02:58:52.033878900Z",
     "start_time": "2023-12-08T02:58:50.162604Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [],
   "source": [
    "for p in resnet.parameters():\n",
    "    p.requires_grad = False\n",
    "\n",
    "resnet.fc = nn.Linear(2048,2,bias=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T03:06:40.128220700Z",
     "start_time": "2023-12-08T03:06:40.063222500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-12-08 11:08:16.114087, epoch:1, i:1, 训练损失:0.012730835784565318\n",
      "2023-12-08 11:08:16.599087, epoch:1, i:5, 训练损失:0.044894033128565006\n",
      "2023-12-08 11:08:17.146135, epoch:1, i:10, 训练损失:0.05240669629790566\n",
      "2023-12-08 11:08:17.692114, epoch:1, i:15, 训练损失:0.051662025126543915\n",
      "2023-12-08 11:08:18.243087, epoch:1, i:20, 训练损失:0.05225635902448134\n",
      "2023-12-08 11:08:18.790115, epoch:1, i:25, 训练损失:0.03732924542643807\n",
      "2023-12-08 11:08:19.684398, epoch:1, i:30, 训练损失:0.025684798034754666\n",
      "2023-12-08 11:08:20.232397, epoch:1, i:35, 训练损失:0.019530052556232972\n",
      "2023-12-08 11:08:20.782446, epoch:1, i:40, 训练损失:0.021906556053595108\n",
      "2023-12-08 11:08:21.329443, epoch:1, i:45, 训练损失:0.023509735004468398\n",
      "2023-12-08 11:08:21.876398, epoch:1, i:50, 训练损失:0.06584830649874428\n",
      "2023-12-08 11:08:22.419444, epoch:1, i:55, 训练损失:0.07017270089550452\n",
      "2023-12-08 11:08:26.855397, epoch:2, i:1, 训练损失:0.0011832397092472423\n",
      "2023-12-08 11:08:27.319397, epoch:2, i:5, 训练损失:0.005135259438644756\n",
      "2023-12-08 11:08:27.863432, epoch:2, i:10, 训练损失:0.04172704229977998\n",
      "2023-12-08 11:08:28.409397, epoch:2, i:15, 训练损失:0.024809091334993188\n",
      "2023-12-08 11:08:28.955432, epoch:2, i:20, 训练损失:0.023560508238998325\n",
      "2023-12-08 11:08:29.502404, epoch:2, i:25, 训练损失:0.018843392452055758\n",
      "2023-12-08 11:08:30.114743, epoch:2, i:30, 训练损失:0.009259440140290693\n",
      "2023-12-08 11:08:30.658743, epoch:2, i:35, 训练损失:0.011940554089166902\n",
      "2023-12-08 11:08:31.201770, epoch:2, i:40, 训练损失:0.014414672892202031\n",
      "2023-12-08 11:08:31.744787, epoch:2, i:45, 训练损失:0.03341645601798188\n",
      "2023-12-08 11:08:32.287787, epoch:2, i:50, 训练损失:0.023181144215843896\n",
      "2023-12-08 11:08:32.830743, epoch:2, i:55, 训练损失:0.012067103250460192\n"
     ]
    }
   ],
   "source": [
    "optimizer = optim.SGD(resnet.parameters(),lr=0.001,momentum=0.9)\n",
    "train_loop(model=resnet.cuda(),loss_fn=loss_fn,optimizer=optimizer,n_epochs=2,train_loader=train_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T03:08:33.597743100Z",
     "start_time": "2023-12-08T03:08:04.529486300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "精度:98.571%\n"
     ]
    }
   ],
   "source": [
    "test_loop(resnet.cuda().eval(),test_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T03:09:10.902706600Z",
     "start_time": "2023-12-08T03:09:04.064538500Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## squeezenet"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SqueezeNet(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2))\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "    (3): Fire(\n",
      "      (squeeze): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (4): Fire(\n",
      "      (squeeze): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "    (6): Fire(\n",
      "      (squeeze): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (7): Fire(\n",
      "      (squeeze): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (8): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "    (9): Fire(\n",
      "      (squeeze): Conv2d(256, 48, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (10): Fire(\n",
      "      (squeeze): Conv2d(384, 48, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (11): Fire(\n",
      "      (squeeze): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (12): Fire(\n",
      "      (squeeze): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (classifier): Sequential(\n",
      "    (0): Dropout(p=0.5, inplace=False)\n",
      "    (1): Conv2d(512, 1000, kernel_size=(1, 1), stride=(1, 1))\n",
      "    (2): ReLU(inplace=True)\n",
      "    (3): AdaptiveAvgPool2d(output_size=(1, 1))\n",
      "  )\n",
      ")\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=SqueezeNet1_1_Weights.IMAGENET1K_V1`. You can also use `weights=SqueezeNet1_1_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "squeezenet = torchvision.models.squeezenet1_1(pretrained=True)\n",
    "print(squeezenet)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T03:11:02.691364500Z",
     "start_time": "2023-12-08T03:11:02.588401Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [],
   "source": [
    "for p in squeezenet.parameters():\n",
    "    p.requires_grad = False\n",
    "squeezenet.classifier = nn.Sequential(\n",
    "    nn.Dropout(0.5,inplace=False),\n",
    "    nn.Conv2d(512,2,kernel_size=(1,1),stride=(1,1)),\n",
    "    nn.ReLU(inplace=True),\n",
    "    nn.AdaptiveAvgPool2d(output_size=(1,1))\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T05:00:49.136903300Z",
     "start_time": "2023-12-08T05:00:49.077899900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-12-08 13:04:53.268073, epoch:1, i:1, 训练损失:0.015233315121043813\n",
      "2023-12-08 13:04:53.586556, epoch:1, i:5, 训练损失:0.0556414474140514\n",
      "2023-12-08 13:04:53.695524, epoch:1, i:10, 训练损失:0.06154889085076072\n",
      "2023-12-08 13:04:54.150881, epoch:1, i:15, 训练损失:0.06677447286519138\n",
      "2023-12-08 13:04:55.249870, epoch:1, i:20, 训练损失:0.028939682245254516\n",
      "2023-12-08 13:04:55.790306, epoch:1, i:25, 训练损失:0.02662619338794188\n",
      "2023-12-08 13:04:56.305302, epoch:1, i:30, 训练损失:0.04700859541242773\n",
      "2023-12-08 13:04:56.736014, epoch:1, i:35, 训练损失:0.020036121593280273\n",
      "2023-12-08 13:04:57.501354, epoch:1, i:40, 训练损失:0.010015444254333322\n",
      "2023-12-08 13:04:57.979102, epoch:1, i:45, 训练损失:0.02520678097551519\n",
      "2023-12-08 13:04:58.495248, epoch:1, i:50, 训练损失:0.017339440367438577\n",
      "2023-12-08 13:04:58.687604, epoch:1, i:55, 训练损失:0.036223516037518326\n",
      "2023-12-08 13:05:05.650959, epoch:2, i:1, 训练损失:0.000931969014081088\n",
      "2023-12-08 13:05:05.713952, epoch:2, i:5, 训练损失:0.006256916204636747\n",
      "2023-12-08 13:05:05.779925, epoch:2, i:10, 训练损失:0.008330481363968415\n",
      "2023-12-08 13:05:05.844921, epoch:2, i:15, 训练损失:0.016383741474287075\n",
      "2023-12-08 13:05:05.920929, epoch:2, i:20, 训练损失:0.02659258944067088\n",
      "2023-12-08 13:05:05.988923, epoch:2, i:25, 训练损失:0.02426556335254149\n",
      "2023-12-08 13:05:06.052921, epoch:2, i:30, 训练损失:0.03273461809889837\n",
      "2023-12-08 13:05:06.118923, epoch:2, i:35, 训练损失:0.019345070286230608\n",
      "2023-12-08 13:05:06.190926, epoch:2, i:40, 训练损失:0.005264207955703816\n",
      "2023-12-08 13:05:06.256924, epoch:2, i:45, 训练损失:0.024369944767518477\n",
      "2023-12-08 13:05:06.318920, epoch:2, i:50, 训练损失:0.021323984522711146\n",
      "2023-12-08 13:05:06.372924, epoch:2, i:55, 训练损失:0.00864007735455578\n"
     ]
    }
   ],
   "source": [
    "optimizer = optim.SGD(squeezenet.parameters(),lr=0.001,momentum=0.9)\n",
    "train_loop(model=squeezenet.cuda(),loss_fn=loss_fn,optimizer=optimizer,train_loader=train_loader,n_epochs=2)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T05:05:06.986922600Z",
     "start_time": "2023-12-08T05:04:20.907463300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "精度:90.000%\n"
     ]
    }
   ],
   "source": [
    "test_loop(squeezenet.cuda().eval(),test_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T05:06:35.803261700Z",
     "start_time": "2023-12-08T05:06:29.078749500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-12-08 13:07:41.975007, epoch:1, i:1, 训练损失:0.0004639320414174687\n",
      "2023-12-08 13:07:42.107006, epoch:1, i:5, 训练损失:0.008806775764308192\n",
      "2023-12-08 13:07:42.219006, epoch:1, i:10, 训练损失:0.005008080436594107\n",
      "2023-12-08 13:07:43.097691, epoch:1, i:15, 训练损失:0.01194187876853076\n",
      "2023-12-08 13:07:44.443411, epoch:1, i:20, 训练损失:0.012393827668645165\n",
      "2023-12-08 13:07:45.512480, epoch:1, i:25, 训练损失:0.030981959944421596\n",
      "2023-12-08 13:07:46.450899, epoch:1, i:30, 训练损失:0.008482345667752352\n",
      "2023-12-08 13:07:47.445184, epoch:1, i:35, 训练损失:0.021873230461708523\n",
      "2023-12-08 13:07:48.711350, epoch:1, i:40, 训练损失:0.02125226885757663\n",
      "2023-12-08 13:07:49.381161, epoch:1, i:45, 训练损失:0.01013134897432544\n",
      "2023-12-08 13:07:50.149825, epoch:1, i:50, 训练损失:0.008367524092847651\n",
      "2023-12-08 13:07:50.681531, epoch:1, i:55, 训练损失:0.0072265542366287926\n",
      "2023-12-08 13:07:55.577868, epoch:2, i:1, 训练损失:0.0009285888888619163\n",
      "2023-12-08 13:07:55.703878, epoch:2, i:5, 训练损失:0.00470392568544908\n",
      "2023-12-08 13:07:55.780875, epoch:2, i:10, 训练损失:0.0034879711525387723\n",
      "2023-12-08 13:07:55.867877, epoch:2, i:15, 训练损失:0.004728256944905628\n",
      "2023-12-08 13:07:55.944882, epoch:2, i:20, 训练损失:0.0018941758518022569\n",
      "2023-12-08 13:07:56.014879, epoch:2, i:25, 训练损失:0.009711815036081877\n",
      "2023-12-08 13:07:56.089902, epoch:2, i:30, 训练损失:0.002194108081642877\n",
      "2023-12-08 13:07:56.159905, epoch:2, i:35, 训练损失:0.00742249681445008\n",
      "2023-12-08 13:07:56.229876, epoch:2, i:40, 训练损失:0.005113176131536337\n",
      "2023-12-08 13:07:56.302875, epoch:2, i:45, 训练损失:0.0041123585094200365\n",
      "2023-12-08 13:07:56.362871, epoch:2, i:50, 训练损失:0.009293452176180753\n",
      "2023-12-08 13:07:56.416868, epoch:2, i:55, 训练损失:0.0033279937522655186\n"
     ]
    }
   ],
   "source": [
    "# 再训练两轮squeezenet\n",
    "train_loop(model=squeezenet.cuda(),loss_fn=loss_fn,optimizer=optimizer,train_loader=train_loader,n_epochs=2)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T05:07:57.078873100Z",
     "start_time": "2023-12-08T05:07:33.398798200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "精度:94.286%\n"
     ]
    }
   ],
   "source": [
    "test_loop(squeezenet.cuda().eval(),test_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T05:08:21.855685Z",
     "start_time": "2023-12-08T05:08:16.482687100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "真实值---预测值\n",
      "小猫 --- 小猫\n",
      "小狗 --- 小狗\n",
      "小猫 --- 小猫\n",
      "小狗 --- 小狗\n",
      "小狗 --- 小狗\n"
     ]
    }
   ],
   "source": [
    "outputs = squeezenet.cuda().eval()(inputs.cuda())\n",
    "_,preds = torch.max(outputs,dim=1)\n",
    "imshow(torchvision.utils.make_grid(inputs))\n",
    "print('真实值---预测值')\n",
    "class_names = ['小猫','小狗']\n",
    "for i,j in zip(labels,preds):\n",
    "    print(class_names[i],'---',class_names[j])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T05:12:52.062632800Z",
     "start_time": "2023-12-08T05:12:48.133478200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
